Spatially Adaptive Regularizer for Mesh Denoising

• Published in: IEEE access 2020-01, Vol.8, p.1-1
• Main Authors: Cheng, Xuan, Zheng, Yinglin, Zheng, Yuhui, Chen, Fang, Lin, Kunhui
• Format: Article
• Language: English
• Subjects: <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Lₚ norm; Computer graphics; Datasets; Face; Geometry; Lp norm; mesh denoising; Neural networks; Noise reduction; Optimization; Regularization; regularizer; Shape; spatially adaptive; Surface treatment
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2987046

Mesh denoising is a fundamental yet not well-solved problem in computer graphics. Many existing methods formulate the mesh denoising as an optimization problem, whereby the optimized mesh could best fit both the input and a set of constraints defined as an Lp norm regularizer. Instead of setting p as a static value for the whole surface, we adopt a dynamic Lp regularizer which imposes two different forms of regularization onto different surface patches for a better understanding of the local surface features. To help determine the appropriate p value for each facet, the guidance is constructed dynamically in a patch-based manner. We compare the proposed method with state-of-the-arts in both synthetic and real-scanned benchmark datasets, and show that the proposed method could produce comparable results to neural network based mesh denoising method, without collecting large training datasets.